用视频自动识别马匹眨眼，评估其情绪与疼痛状态。
Horse Eye Blink Detection and Classification for Equine Affective State Assessment
- 基于YOLOv12、光流阈值和VideoMAE三种方法检测眨眼
- 眨眼分类宏F1达0.898，二分类准确率0.926
- 适合动物福利监测与智能养殖场景
自动化检测马匹面部动作单元（AUs）是评估马匹疼痛与情绪状态的有前景方向，但尚未充分探索。半闭眼和全闭眼动作是疼痛与压力的显著指标，但由于属于微表情，其细微特征易被肉眼忽略，需逐帧视频分析才能识别，因此从视频中可靠实现自动化检测极具挑战。本文开发并评估了三种从马匹视频中自动分类眨眼的方法：基于帧的YOLOv12检测器、光流幅度阈值法，以及微调的VideoMAE模型，均在公开数据集上测试。结果显示，眨眼分类的宏F1得分为0.898，二分类检测准确率达0.926。结果凸显了细粒度动作单元检测在马匹福利监测中的潜力与内在挑战。
原文摘要 · Abstract (English)
Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half and full-blink movements are recognised indicators of pain and stress, but as micro-expressions, their subtle, fine-grained nature makes them easily missed by the naked eye and only discernible through frame-by-frame video inspection, making reliable automated detection from video a particularly demanding task. We develop and evaluate three methods for automated blink classification from horse videos: a frame-based YOLOv12 detector, an optical flow magnitude thresholding approach, and a fine-tuned VideoMAE model, tested on a publicly available dataset. We achieve a macro-F1 score of 0.898 when doing blink classification and 0.926 on binary blink detection. Our results highlight both the potential and the inherent challenges of fine-grained AU detection for equine welfare monitoring.
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